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AssigneeAdobe Inc.
InventorsChristian Perez, Eunyee Koh, Ashley Rosie Weiling Chen, Ankita Pannu
Estimating the term…
Techniques are disclosed for accurately identifying distinct physical user devices in a cross-device context. An example embodiment applies a multi-phase approach to generate labeled training datasets from a corpus of unlabeled device records. Such labeled training datasets can be used for training machine learning systems to predict the occurrence of device records that have been wrongly (or correctly, as the case may be) attributed to different physical user devices. Such identification of improper attribution can be particularly helpful in web-based analytics. The labeled training datasets include labeled pairs of device records generated using multiple strategies for inferring whether the two device records of a pair of device records represent the same physical user device (or different physical user devices). The labeled pairs of device records can then be used to train classifiers to predict with confidence whether two device records represent or do not represent the same physical user device.
A computer-implemented method to generate training data for training a classifier for identification of physical user devices, the method comprising:
A system to generate training data for training a classifier for identification of physical user devices, the system comprising:
A computer program product including one or more non-transitory machine readable mediums encoded with instructions that when executed by one or more processors cause a process to be carried out for generating training data for training a classifier for identification of physical user devices, the process comprising:
Dependencies are inferred from the claim text.